
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "244"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 19 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 19 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459996	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	1.835364	-1.123079	-0.379642	-1.390725	-0.729349	-0.514624	1.721171	4.192055	0.5675	0.6101	0.3986	nan	nan
2459995	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	2.181612	-1.272052	-0.900302	-1.233858	-0.900108	-0.403846	2.733053	5.540825	0.5602	0.6072	0.3895	nan	nan
2459994	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	2.201838	-1.191698	-0.792163	-1.100212	-0.862071	-0.578922	1.703700	5.452429	0.5517	0.5997	0.3860	nan	nan
2459991	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	3.169393	-1.287156	-0.867859	-0.842097	0.016104	-0.930368	1.978528	5.158827	0.5462	0.5905	0.3961	nan	nan
2459990	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	2.566454	-0.795183	-0.866255	-0.742652	-0.303154	-0.413397	1.671032	4.940923	0.5465	0.5949	0.3927	nan	nan
2459989	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	2.243184	-0.706861	-0.792826	-0.723602	-0.350562	-0.579791	1.570773	3.833148	0.5453	0.5973	0.3954	nan	nan
2459988	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	2.733908	-0.943305	-0.983426	-0.803152	0.186732	-0.105137	1.264281	4.339584	0.5477	0.5994	0.3893	nan	nan
2459987	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	1.949388	-1.048534	-0.850940	-1.041502	-0.389847	-0.766672	2.503357	7.402845	0.5539	0.6024	0.3830	nan	nan
2459986	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	2.828688	-1.156623	-0.909425	-0.857540	0.289965	-0.599221	0.694706	2.276353	0.5848	0.6333	0.3420	nan	nan
2459985	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	2.556703	-1.354583	-0.831789	-1.056895	-0.561258	-0.268809	3.454778	9.769156	0.5582	0.6061	0.3911	nan	nan
2459984	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	2.506404	-1.231579	-0.763837	-1.093164	-0.655182	-1.834456	0.170512	1.996682	0.5740	0.6207	0.3697	nan	nan
2459983	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.332346	-0.786290	-0.815193	-0.712927	-0.547768	-0.544480	0.984135	2.716747	0.5816	0.6288	0.3390	nan	nan
2459982	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	1.120295	0.181332	-0.430045	-1.060953	-0.798004	-1.079652	-0.265069	-0.983019	0.6518	0.6722	0.2728	nan	nan
2459981	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	4.533685	2.010557	1.509768	-0.889030	3.369733	0.999377	3.255281	7.152352	0.4728	0.5709	0.3747	nan	nan
2459980	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	3.996034	1.704772	1.282303	-0.615990	2.848771	0.760668	0.399954	0.131353	0.5499	0.6255	0.3040	nan	nan
2459979	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	4.706397	1.881074	1.078850	-0.769938	2.486509	-0.527201	2.146836	5.740685	0.4632	0.5673	0.3776	nan	nan
2459978	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	4.830764	2.159395	1.200454	-0.817156	2.684658	0.721310	2.722662	7.870353	0.4649	0.5659	0.3825	nan	nan
2459977	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	4.456312	1.866048	1.316230	-0.599906	2.889108	0.105032	2.936111	7.396361	0.4325	0.5260	0.3384	nan	nan
2459976	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	4.650622	2.111204	1.331888	-0.789617	2.259660	0.773306	1.992313	6.314057	0.4775	0.5750	0.3730	nan	nan

Antenna Season Report Notebook¶

Antenna 244 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 244, Node N20:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 244: 2459996

Antenna 244: 2459995

Antenna 244: 2459994

Antenna 244: 2459991

Antenna 244: 2459990

Antenna 244: 2459989

Antenna 244: 2459988

Antenna 244: 2459987

Antenna 244: 2459986

Antenna 244: 2459985

Antenna 244: 2459984

Antenna 244: 2459983

Antenna 244: 2459982

Antenna 244: 2459981

Antenna 244: 2459980

Antenna 244: 2459979

Antenna 244: 2459978

Antenna 244: 2459977

Antenna 244: 2459976

Antenna Season Report Notebook¶

Antenna 244 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 244, Node N20:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 244: 2459996

Antenna 244: 2459995

Antenna 244: 2459994

Antenna 244: 2459991

Antenna 244: 2459990

Antenna 244: 2459989

Antenna 244: 2459988

Antenna 244: 2459987

Antenna 244: 2459986

Antenna 244: 2459985

Antenna 244: 2459984

Antenna 244: 2459983

Antenna 244: 2459982

Antenna 244: 2459981

Antenna 244: 2459980

Antenna 244: 2459979

Antenna 244: 2459978

Antenna 244: 2459977

Antenna 244: 2459976

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶